3rd SFERA Summer School Solar resource forecasting

Transcription

1 3rd SFERA Summer School Solar resource forecasting M. Schroedter-Homscheidt with contributions from G. Gesell, L. Klüser, H. Breitkreuz, A. Oumbe, N. Killius,T. Holzer-Popp DLR, German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D Wessling C. Hoyer-Klick, M. Wittmann, DLR, Institute for Technical Thermodynamics (ITT), Stuttgart B. Pulvermüller, Solar Millennium AG, Erlangen; M. Hoyer, D. Glumm, Flagsol GmbH B. Kraas, CSPServices (former Solar Millennium AG) J. Remund, Meteotest Folie 1

2 Outline Forecast requirements Basic meteorological terminology and forecast methodolgies Numerical weather prediction verification measures, spatial error correlation Do we need aerosol forecasts? Possibilities of ground measurements? Satellite-based nowcasting Some thoughts on the economic value of forecasts (only market participation case) Credits to EC Global Monitoring for Environment and Security (GMES) MACC Atmosphere core service preparations project ENDORSE (Energy downstream services) project ESA Integrated Application Program CSP-FoSyS project EC DG ENV EnerGEO project Slide 2

3 This talk concentrates on Direct normal irradiance forecasting Focus on hourly resolution Less than hourly resolved forecasting No solar power output forecast = fct (power plant state, plant technology) No other meteo parameters (wind gust, temperature, wind) This overview concentrates on principles It does not provide many information on typical accuracies (look into the additional slides) Slide 3

4 Use cases in grid integration studies Transmission System operator (TSO) point of view grid stabilisation 15 minutes interval up to 1-2 hour forecast needed Power plant operator point of view (especially for CSP) Slide 4

5 Based on 1 year measurements at the Andasol site Slide 5

6 Reduction of direct irradiance Clouds up to 100% Water vapour 10-15% aerosols 20-25%, up to 100% Slide 6

7 Hindcast historical forecasts good for assessments/development Nowcast Shortterm forecast Day ahead forecast Medium range forecast Climate scenarios Deterministic forecast Ensemble forecast = up to 3 hours = up to 6 hours = up to 48 hours = up to 10 days NOT a forecast or prediction a scenario describes a possible state e.g or 2100 a single weather model run 30 or 50 weather model runs with small changes probabilistic evaluation ECMWF Slide 7

8 CSP operator point of view example case Spain Day ahead Intra-day 48h deterministic forecast, 00 UTC run + ensemble forecast + model output statistics Aerosol forecasts (chemical transport model) Nowcasting clouds (IR satellite) Large open question: Use and value of forecasts in plant operations besides day ahead and intraday markets. Not treated so far Nowcasting irradiance, cloud movement vectors aerosol monitoring Stochastic methods Slide 8

9 Methods and results Slide 9

10 Physical methods Apply physical principles (atmospheric dynamics, thermodynamics, radiative transfer, optical properties) Numerical weather prediction gas law, hydrostatic equation (air density/pressure with height), conservation of mass, equation of motion, thermodynamic equation conservation of moisture, physical processes like condensation, evaporation radiative processes on a 3D-grid use of ground, in-situ and satellite observations (data assimilation) -> temperature, wind, clouds, movement of clouds -> radiation In recent models: also aerosol forecast as input to radiation Advantages > few hours forecast horizon Physical understanding Short-term NWP have to be developed for radiation (exists for other parameters) ECMWF Slide 10

11 Physical methods Satellites optical measurements in visible and infrared spectrum optical properties like refractive indices radiative transfer modeling -> radiation Forecast based on either NWP through data assimilation or on stastical procedures (next slide) Slide 11

12 Stochastic learning techniques Characteristics: rely on past data to train models, no/little physical assumptions assumption that future irradiation can be predicted based on historical patterns assume persistence in opacity, direction and velocity of clouds Methods persistence approach yesterday == tomorrow (2 day sometimes needed) auto-regressive models artificial intelligence/neural network cloud motion vectors (cloud camera/satellites) Advantages Good for intra-hour or nowcasting cheap Slide 12

13 Characteristics of solar forecasting techniques Technique Sampling rate Spatial resolution Spatial extent Maximum Suitable Forecast horizon Application Persistence High One point One Point Minutes Baseline Whole Sky Imagery 30 sec 10 to 100 meters 3 8 km radius 10s of minutes Ramps, regulation Geostationary satellite imagery Numerical weather prediction (NWP) 15 min 1 km 65 S 65 N 5 hours Load following 1 hour 2 50 km Worlwide 10 days Unit commitment regional power prediction Source: Remund, 2012 Slide 13

14 Circum-solar taken into account? Typical textbook definition: the radiant flux collected by a surface normal to the direction of the Sun, within the extent of the solar disk only (half-angle δ = ) Three worlds : three definitions of DNI! World of Radiative Models (e.g. 6S, libradtran): Solar source is a Dirac NWP, some SAT World of Physical Measurements: definition of DNI by the corresponding measurement device. From WMO CIMO Guide (2008): Direct solar radiation is measured by means of pyrheliometers, the receiving surfaces of which are arranged to be normal to the solar direction. By means of apertures, only the radiation from the sun and a narrow annulus of sky is measured, the latter radiation component is sometimes referred to as circumsolar radiation or aureole radiation Some stochastic methods World of Solar Energy Conversion: definition of DNI by the corresponding concentrating solar system (0.9 to 5 ) Slide 14

15 Numerical weather prediction Model runs initiated two to four times per day (e.g. 0, 6, 12 and 18 UTC) initial conditions from satellite, in-situ, ground observations Pre-processed and interpolated to the 3D grid resolution of global NWP models is coarse (15 to 90 km) mesoscale or limited area models: limited geographical area with higher resolution (up to few km) Trouble with mesoscale models: Place clouds still at the wrong place and have RMSE worse than global ECMWF model. ECMWF = European Centre for Middle-Range Forecasts Since 2011 ECMWF provides also direct irradiance forecasts Most models provide only global -> statistical global to direct conversion needed Slide 15

16 Verification ECMWF/DLR : Scatterplots for hourly values 2 day persistence, 2005 ECMWF/DLR, 2005 Slide 16

17 Verification ECMWF/DLR Skill score = (RMSE fc RMSE pers )/RMSE pers Bad morning due to 3-hourly forecast Better than persistence worse Slide 17

18 Verification ECMWF/DLR biases = fct (time of day) underestimation thin ice clouds Slide 18

19 Verification ECMWF/DLR RMSE = fct (time of day) Slide 19

20 Verification ECMWF/DLR Local correlation coeff. = fct (time of day) Slide 20

21 Verification ECMWF/DLR Same parameters: Biases, RMSE, rel RMSE, local correlation coeff., skill scores = fct (month of year) Temporal lag errors (if any) Slide 21

22 Perfect forecast capability for a renewable resouce No reserve power needed to cope with forecasting errors Reduced costs from the grid operator point of view Slide 22

23 Perfect forecast capability for a renewable resouce not the case for solar reserve power needed to cope with forecasting errors Costs need to be taken into account from the grid operator point of view Is there any spatial variability in these costs? 23 Slide 23

24 Go from a single location to a list of validation sites European BSRN sites Slide 24

25 Assessment based on satellite measurements as ground truth 2 day persistance Rel. RMSE, 2008 ECMWF Slide 25

26 ECMWF skill score, 12 UTC, 2008 Slide 26

27 ECMWF, error spatial correlation vs. cross, 2008 Slide 27

28 Model output statics (MOS) It does: Spatio-temporal interpolation and smoothing (from grid) Local adaptation + bias correction Multi-NWP-model combination General problems Update for each NWP model change (happens frequently, monitoring needed) Update for each location needed Specific problems for DNI Generate DNI out of cloud coverage, global irradiance, other parameters Most input models provide no information about aerosols Temporal interpolation from global 3-hourly to hourly Finding for Spain: ECMWF is still performing better than existing commercial MOS systems Slide 28

29 Aerosole typical: - 25% direct irradiance - 5% global irradiance Greece August 25, 2007 Northeastern Egypt, Israel, and western Jordan 1000 February 24, DNI [W/m2] :00 6:00 12:00 18:00 0:00 Northern India, Nepal, and Bangladesh February 5, 2006 July 16, 2003 Slide 29

30 Exceedance days per year direct irradiance desert dust only Number of days with a direct irradiance extinction due to dust above 30% based on a DLR/MATCH aerosol model for the EUMENA region Slide 30

31 User requirements for aerosol accuracy continental Basic input for CSP: DNI (Direct Normal Irradiance) each AOD and each SZA, minimum deviation on AOD leading to x% deviation on DNI done for 5%, 10%, 20% desert Minimum DNI : 10 W/m2 urban Slide 31

32 User specific validation approach ΔAODm(sza, AOD) forecast AOD: daily mean, 2D persistence, ECMWF forecast AERONET AOD Computation of ΔAODc(sza, AOD) leading to a predefined x% ΔDNI, at each 2 SZA and 0.1 AOD Interpolation of ΔAODc(sza, AOD) and comparison Check for each hour If ΔAOD > ΔAOD crit If ΔAODc > ΔAODm then nc+1 else nc+0 Deriving the corresponding ΔDNI = ΔDNI by fct the (sza, Interpolation AOD, of ΔDNIc(sza, AOD, ΔAOD) ΔAOD) Computation of ΔDNIc(sza, Libradtran AOD, ΔAOD) forward due to ΔAOD, at each 4 SZA, 0.1 AOD and LUT ΔAOD For all timeseries, at each station % of hours where ΔAODm leads to x% ΔDNI relative bias and relative RMS on DNI DNI = Direct normal irradiance Slide 32

33 Does intra-day variation matter in our case? % of hours where the deviation on DNI due to intra-day variability is higher than 5% % of hours where the deviation on DNI due to intra-day variability is higher than 20% Slide 33

34 Is a 2 day persistence forecast sufficient? % of hours where the deviation on DNI due 2 day persistence forecasting is higher than 20% Slide 34

35 Cloud camera, total sky imagery very short-term (minutes ahead) predictions of future cloud patterns does not account for cloud development and dissipation limited to the field of view of the sky imager may use multiple imagers at different locations actual look-ahead time depends upon the cloud velocity and cloud height low and fast clouds the forecast horizon may only be 3 minutes high and slow clouds it may be over 30 minutes generally horizons between 5 to 20 minutes are typical. multiple height cloud layers have different motion vectors extremely important for DNI Slide 35

36 Ratio of red and blue channel intensity a partly cloudy day UC San Diego solar forecasting sky imager high RBR are classified as cloudy No experience with DNI available so far 30 second GHI ramp forecast against 1 sec GHI measurements day with cumulus clouds Mathiesen, Kleissl, 2012 Slide 36

37 Representativeness of nearby pixels PSA -1 hours lag scale from 0.65 to 0.73 (42-53% explained) PSA -2 hours lag scale from 0.48 to 0.55 (23-30% explained) Slide 37

38 Sun point information cloud camera 20 min forecast horizon high resolution satellite several hours forecast type of cloud less spatial resolution Slide 38

39 Satellite based information about typical weather conditions at the power plant site Slide 39

40 Cloud compactness indicators overcast Cologne, D overcast Almeria, E Few large clouds Broken clouds Broken clouds Scattered clouds Fractal box dimension Scattered clouds isolated clouds No of cloud elements in a 49x49 window isolated clouds Slide 40

41 Cloud period duration statistics 50 Number distribution [%] Cologne 50 PSA Cloudy period duration [h] Cloudy period duration [h] 18% <= 15 min 39% < 1 h 90% < 9.75 h 98% < 13.5 h 28% <= 15 min 59% < 1 h 90% < 4.5 h 98% < 9.25 h Slide 41

42 Nowcasting based on satellite measurements Slide 42

43 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 1/8 Slide 43

44 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 2/8 Slide 44

45 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 3/8 Slide 45

46 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 4/8 Slide 46

47 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 5/8 Slide 47

48 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 6/8 Slide 48

49 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 7/8 Slide 49

50 Nowcasting and short-term forecasting based on EO Example: cloud fields from Meteosat Second Generation HRV channel for Greece (source DLR-IPA), basis for short-term forecasts cloud motion vectors 8/8 Slide 50

51 METEOSAT SECOND GENERATION What can be seen more? More spectral information allows assessment of physical cloud parameters 15 min resolution instead of 30 minutes Pixel size 3 km at nadir; 4x5 or 5x6 km in Europe Upcoming Key Questions: How do I choose technical features of the power plant? (Engineering) How do I operate my power plant? (Operation & Maintenance) Copyright EUMETSAT Slide 51

52 Strategy to use nowcasting at the power plant Type A 48 h forecast made by weather service yesterday Type B Satellte images Now 45 Min Now 30 Min Now 15 Min now Human forecast with knowledge about upcoming cloud fields Type C Now 45 Min Satellte images Now 30 Min Now 15 Min now Automatic 8h-nowcast of upcoming cloud fields now Automatic 8h-nowcast of upcoming DNI values Slide 52

53 Photo-like quick look Low clouds white, high clouds turquoise blue Looks natural Looks known to the operator We just see iced tops of clouds. We can t distinguish thick ice clouds (e.g. 10% DNI) from thin ice clouds (e.g. 50% DNI) Can t see dust over land Slide 53

54 Dust and cirrus special colors Dust pink, thin cirrus black, available at night time!!! 3th May 2011, 0900 UTC Slide 54

55 Automatic nowcasting which DNI to expect? DNI forecast Bias DNI [W/m2] ECMWF_enh ECMWF_enh_ncr time lag in hours APOLLO cloud retrieval for MSG EUMETSAT/DLR EUMETSAT/DLR EUMETSAT/DLR EUMETSAT/DLR Cloud mask water clouds thin cirrus clouds 15 min update Receptor model cloud mask forecast Day and night!! Slide 55

56 Some comments on the economic value of forecasts. Slide 56

57 Kraas et al., SolarPaces 2010; submitted to Solar Energy Based on Meteologica MOS July 2007 to December 2009 Benchmark = average penalty Slide 57

58 Benchmark = average penalty Slide 58

59 Slide 59

60 Conclusions User requirements and meteorological basics Numerical weather prediction verification measures have been explained NWP can be used, potential for improvements is also large First results on spatial correlation of forecast errors Global maps need of hourly aerosol forecasts, need of aerosol forecasts at all regionally different Ground measurements are difficult to use in the multi-hour forecast horizon Satellites help to characterize any location in advance with respect to typical cloud behaviour Satellite-based nowcasting provides decision support to the plant operator both visual and quantitatively Value of forecasts quantified for the market participation case Value of forecast improvments is a nearly linear effect also if taking a non-linear market into acount not obvious Slide 60

61 References Beyer, H.G., Martinez, J.P., Suri, M. et al., MESOR Report on Benchmarking of Radiation Products. Deliverable 1.1.3, Management and Exploitation of Solar Resource Knowledge (MESOR), European Commission 6 th framework programme, Contract Number Breitkreuz, H., Schroedter-Homscheidt, M., Holzer-Popp, T., Dech, S., Short Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling. Journal of Applied Meteorology and Climatology, 48 (9), pp DOI: /2009JAMC Beyer HG, Polo Martinez J, Suri M, Torres JL, Lorenz E, Müller SC, Hoyer-Klick C and Ineichen P. D Report on Benchmarking of Radiation Products. Report under contract no of MESoR, Available for download at (January 12, 2012). Slide 61

62 Lara-Fanego, V., Ruiz-Arias, J.A., Pozo-Vázquez, D., Santos- Alamillos, F.J., Tovar-Pescador, J., Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain). Solar Energy, 2012 Mathiesen P, Kleissl J. Evaluation of numerical weather prediction for intra-day solar forecasting in the continental united states. Solar Energy. 2011;85(5): Perez R, S Kivalov, J Schlemmer, K Hemker Jr., D Renne, TE Hoff, Validation of short and medium term operational solar radiation forecasts in the US, Solar Energy, 2010 Remund, J., Photovoltaic and solar forecasting state of the art, IEA PVPS report, section 3.4, 2012 (in edit stage at the moment) Wittmann, M., Breitkreuz, H., Schroedter-Homscheidt, M., Eck, M., Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 1, 2008 Slide 62

63 Additional slides dealing with accuracies Slide 63

64 Source Perez et al, 2010: RMSE for GLOBAL IRRADIANCE forecasts US SURFRad stations Similar results for DNI are not available so far Slide 64

65 Example on forecast metrics ECMWF global irradiance forecasts 0.25 degree spatial resolution Day 2 forecast to deal with day ahead market 1 hourly resolution generated from 3 hourly ECMWF output for day ahead/intra day markets GHI2DNI conversion scheme for concentrating solar power BSRN, PSA and Andasol-3 surface radiation ground measurements MSG SOLEMI irradiance retrievals MESOR QC/Benchmarking standards (see references) Slide 65

66 Verification of ECMWF-based direct irradiance forecasts, PSA, 2005 Day 2, PSA, 2005 GHI_g>0 Obs. DNI mean [Wh/m²] or [W/m²] Bias [Wh/m²] or [W/m²] 2 day Persistence Daily sum ECMWF/DLR Daily sum Persistence hourly ECMWF/DLR hourly RMSD [Wh/m²] or [W/m²] Rel. RMSD [%] Corr Slide 66

67 Quantification sat-based nowcast impact vs. latest deterministic ECMWF forecast (case of Southern Spain) RMSE NC - RMSE ECMWF (W/m2) NC 6 UTC NC 7 UTC NC 8 UTC NC 9 UTC NC 10 UTC NC 11 UTC NC 12 UTC NC 13 UTC NC 14 UTC NC 15 UTC NC 16 UTC NC 17 UTC NC 18 UTC 350 forecast day 2 (UTC), first hour of nowcasting RMSE (W/m2) NC 6 UTC NC 7 UTC NC 8 UTC NC 9 UTC NC 10 UTC NC 11 UTC NC 12 UTC NC 13 UTC NC 14 UTC NC 15 UTC NC 16 UTC NC 17 UTC NC 18 UTC ECMWF 2007, all days Nowcasting only if discrepancy between ground measurement and forecast is observed Mainly bias reduction!!! forecast day 2 (UTC), first hour of nowcasting Slide 67

68 Duration of positive impact of satellite nowcasting Case of Southern Spain no. of hours with reduced RMSE forecast day 2 (UTC), first hour of nowcasting Typical pos. impact global irradiance, Europe Typical values in Europe for direct irradiance -> we don t know. Slide 68